Listen & observe
Semi-structured interviews, focus groups, contextual inquiry, observation, and case studies.
RESEARCH
Understanding people.
Questioning AI.
I investigate how AI fits into educational contexts shaped by culture, language, and institutional practice. My work connects qualitative inquiry with responsible technology design.
RESEARCH PROGRAM
Three connected lines of inquiry.
01 / MASTER’S THESIS
Culturally responsive AI assessment
My thesis examines culturally responsive AI assessment with educators, students, and administrators. Research phases are complete; the final draft is in progress.
Mixed methods · Four-country interviewsExplore the thesis →
02 / AI & LEARNING
Language, literacy & generative AI
Understanding how multiple language learners use AI, and how faculty interpret its role in learning.
Interviews · Task-based studyExplore the research →
03 / EXPLAINABLE AI
Transparency in educational AI
A published team project examining course-grounded AI assistance and source transparency.
Human-centered design · RAGExplore the research →
A SHARED FOUNDATION
Inclusion begins
with representation.
My co-authored work on diverse role models in STEM examines whose experiences are represented in higher education. It considers the underrepresentation of people of color, individuals with disabilities, and LGBTQIA+ communities among faculty.
The paper positions faculty diversity as essential to equitable educational opportunity, student belonging, and retention. These questions complement my work on AI: the social and institutional environment matters alongside the technology itself.
My role: co-author with Yugo Iwamoto, April Roberts, and Catherine Beaton.
Read the IEEE ISEC 2025 paper (opens in a new tab)HOW I WORK
Methods grounded in context.
Analyze & interpret
Thematic analysis, qualitative research, secondary data analysis, and mixed-methods inquiry.
Design & evaluate
Survey design and validation, quantitative data analysis, usability testing, and human-centered prototyping.
RESEARCH INTERESTS
Questions I’m exploring.
How do culture, language, and institutional context shape people’s use of AI? I investigate these questions in education to inform how AI systems are designed and evaluated.
01 How can institutions evaluate AI readiness in ways that reflect local cultural and pedagogical contexts?
02 How can learning environments support critical AI use while making learners’ own understanding visible?
03 How can explanations and source transparency support informed use of educational AI?